The core elements of the ELM objective (REF ) are not without precedent.
For example, the idea of regularising embeddings has been widely explored in the area of contrastive learning [1]}, [2]}, [3]}.
Similarly, the idea of having the pull term underpins Fisher linear discriminant analysis [4]}.
However, the key to ELM's success in long-tailed problems is enforcing margins in both the logit and embedding space, and having these margins be sensitive to the label distribution \(\mathbb {P}(y)\) .
We now
detail the relevant strands of prior work,
and
delineate the key differences to ELM.
(See Table REF .)
<TABLE>